Content item selection

ABSTRACT

A content item, e.g., an icon or advertisement content, is selected for placement in a display environment (e.g., on a map or adjacent to a map) in response to a request for the display environment based on a probability that the content item is relevant to a user that is requesting the display environment. The selection is facilitated by content targeting data (e.g., feature selection and query submission) that can be received from user devices while the map space is presented.

BACKGROUND

This subject matter of this specification relates to informationselection.

The Internet facilitates easy access to on-line mapping systems formillions of people. These on-line mapping systems can provide detailedmaps of geographic regions at a variety of zoom levels. Additionally,the dynamic nature of these on-line mapping systems can facilitate thetargeted presentation of content, such as advertisements. For example, acoffee retailer can provide advertisements in the form of selectableicons that are rendered on a map page that is displayed on a clientdevice, such as a computer. Mousing over the icon can reveal additionaladvertising information, such as the coffee retailer's business name andcontact information, including the address.

The content items can be selected for presentation with the map based,for example, on the portion of the map that is presented. However, whencontent items are presented based solely on the address of theadvertising entity, it is possible that the advertisement selected isnot relevant to the user. For example, the user may be viewing a map ofa potential vacation destination, while the presented content item isfor a plumber that has a business that is located in the displayedportion of the map. Displaying advertisements that are irrelevant to auser's interests can degrade the user experience.

SUMMARY

The subject matter of this specification relates to selection of contentitems, e.g., advertisements, for display in a display environment (e.g.,on maps or adjacent to maps). The content items can be selected based onthe probability that the content items are relevant to users thatrequest the display environment. The probability that the content itemsare relevant can be determined, for example, based on input data thathas been received from users while the display environment is presented.The input data can correspond to user interactions in a displayenvironment (e.g., mouse click, feature selection, submitted queries,user queries received while the display environment is presented, etc.).Content targeting data (e.g., topic data) can be identified based on theinput data and content items can be selected for presentation based onthe content targeting data.

In general, one aspect of the subject matter described in thisspecification can be embodied in a method that includes the actions ofreceiving a request for a map space, wherein the map space is a portionof a map for presentation in a display region on a user device;identifying content targeting data associated with the map space, thecontent targeting data based on input data that have been received fromdisplay interfaces in response to presentations of the map space in thedisplay regions prior to the request for the map space; identifying acontent item for display with the map space based on the contenttargeting data; and providing the content item for display in thedisplay region on the user device in response to the request for the mapspace. Other embodiments of this aspect include corresponding methods,apparatus, and computer program products.

Another aspect of the subject matter described in this specification canbe embodied in a method that includes the actions of receiving inputdata from user devices, the input data having been input in displayinterfaces displayed on user devices; associating the input data withmap spaces, each data input being associated with a map space that wasdisplayed in a display region on a user device when the data input wasinput in the display region at the user device; for each map space,generating content targeting data based on the associated input data;and providing access to the content targeting data for each map space tofacilitate identification of content items for display with the mapspaces. Other embodiments of this aspect include corresponding methods,apparatus, and computer program products.

The details of one or more implementations of the subject matterdescribed in this specification are set forth in the accompanyingdrawings and the description below. Other features, aspects, andadvantages of the subject matter will become apparent from thedescription, the drawings, and the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an example online map environment.

FIGS. 2A and 2B are examples of a map environment at different zoomlevels and for which advertisements can be selected for presentation.

FIG. 3 is a block diagram of an example content item selection system.

FIG. 4 is a flowchart of an example process of selecting a content itemfor presentation with a map.

FIG. 5 is a flowchart of an example process of generating contenttargeting data based on input data.

FIG. 6 is block diagram of an example computer system that can be usedto facilitate positioning of content on online maps.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION §1.0 Overview

The subject matter of this specification relates to selection of contentitems, e.g., advertisements, in a display environment. In someimplementations, the content items can be selected, for example, basedon a probability that the content item is relevant to interests of auser that requests a particular map space (e.g., portion of a map). Theprobability that the content item is relevant to the interests of a userthat is requesting the map space can be determined, for example, basedon input data that have been received from user devices in response toprevious presentations of the map space. The input data can be data thatis representative of user actions. The user actions can be, for example,selection of map features (e.g., a display option) by the user, orsubmission of search queries while the map space was displayed.

The input data can be used to generate content targeting data, e.g.,topic data that correspond to the input data and correspondingprobabilities that the topics are related to the input data. The contenttargeting data can be provided to a content selector (e.g.,advertisement server) that can identify a content item for presentationwith the map space based on the topics and the correspondingprobabilities. The selected content item can be received from thecontent item selector and presented with the requested map space. Insome implementations, the content item can be presented in the map spacethat is presented. In other implementations, the content item can bepresented adjacent to the map space in a display environment.

In a particular example, a user may request a map space for a city thatincludes a large number of wineries, e.g., Napa Valley. Therefore, inputdata may have been received through search queries for “winery” thatwere input by previous users while the map space was presented. In someimplementations, these queries for “winery” can be used to identifyrelated topics. For example, semantic rules can be used to determinethat the topics “wine,” “winery tour,” and “pinot noir” are related to“winery.” Additionally, a probability that each topic corresponds to thequery “winery” can be determined. For example, “wine,” “winery tour,”and “pinot noir” may have respective probabilities of 0.3, 0.2, and 0.1of being related to the query “winery”, respectively.

These probabilities can be adjusted to represent a probability that therespective topics are relevant to any given user the requests the mapspace. In one example, a ratio of the number of input data received fromusers that are related to the content targeting data relative to allinput data received from users can be determined. In turn, theprobability that that the respective topics are relevant to any givenuser that requests the map space can be determined, for example, bymultiplying the ratios and the respective probabilities. Continuing withthe example above, if 30% of the input data received from users isrelated to the topic “wine” then there can be a 9% probability (e.g.,0.3*0.3) that a content item presented based on the topic “wine” will berelevant to any given user that requests the map space.

The content targeting data can be provided to a content item selector.In turn, the content item selector can identify a content item to bepresented based on the content targeting data. For example, a contentitem with content related to “wine” can be identified for presentationwith the map space. In turn, the content item can be presented in themap space or adjacent to the map space.

§1.1 Map Advertising

FIG. 1 is a block diagram of an example online map environment 100. Themap environment 100 can facilitate the serving of content items fordisplay with a map. Although this specification uses advertisements asexample content items and advertising servers as example contentservers, many other content items can also be served and presented fromother servers, such as icon links to video clips; information links orinformation snippets; picture links; etc.

In some implementations, advertisers 102 can connect to an advertisementserver 104 to upload advertisements 103, track advertising statistics,bid for advertising space, or otherwise interact with the advertisementserver 104. The advertisers 102 can connect to the advertisement server104 through the network 105. The network 105 can be a wide area network,local area network, the Internet, or any other public or privatenetwork, or combination of both.

The advertisements 103 may be in the form of graphical advertisements,text only advertisements, image advertisements, audio advertisements,video advertisements, advertisements combining one of more of any ofsuch components, etc. The advertisements 103 may also include embeddedinformation, such as links, meta-information, and/or machine executableinstructions. The advertisements 103 can be formatted for presentationin maps 109. The advertisements 103 can be stored in an advertisementstore 106 that is connected to the advertisement server 104.

The advertisements 103 can be displayed with a map 109 that is presentedin a display environment on a user device 110. The map can be requesteddirectly from a map server 112 that provides a map user interface on auser device 110 when the user device 110 connects to the map server 112and requests a map that is generated from map data stored in a map store114.

Alternatively, the map 109 can be requested from a publisher 108 inresponse to a user device 110 requesting content from the publisher 108.Example publishers 108 can be network content providers that publishwebsites. The publishers 108 receive requests for content from the userdevices 110 and present content in response to the requests. In responseto requests, the publishers 108 can include maps 109 with the content,thereby allowing the user devices 110 access to maps 109 through thepublisher's website.

For example, the publisher 108 may be a business entity and may providea map to its location on the home page of the publisher 108. In responseto the map request by the publisher 108, the map server 112 selects datafor the requested map from the map store 114 for presentation, andprovides the selected data to either the publisher 108 or the requestinguser device 110.

User devices 110 can connect to the publishers' websites or the mapserver 112 through the network 105 utilizing any device capable ofcommunicating in a computer network environment and displaying retrievedinformation. Example user devices 110 include a web-enabled handhelddevice, a mobile telephone, a set top box, a game console, a personaldigital assistant, a navigation device, or a computer.

The request for a map 109 can also include or generate a request foradvertisements 103. The map server 112 can request advertisements 103from the advertisement server 104. In some implementations, theadvertisements 103 selected for delivery to the map server 112 can beselected based on their association with the map 109 selected by the mapserver 112 or any other advertisement selection criteria, such as asearch query submitted by a user viewing the map space. For example, theadvertisements 103 can be associated with the map 109 if the advertiser102 has a retail location within the region of the map 109 selected.Similarly, the advertisements 103 can be presented in response to theuser submitting a search query when the user is viewing the map.

The advertisements 103 can be placed on the map 109 at a locationrepresentative of the location associated with the advertiser 102 (e.g.,as an overlay near a map of the retail location of the advertiser) or ina designated portion of the display environment (e.g., a map sidebar).

The selection of advertisements based on a user's action while the useris viewing the map, however, requires the user to submit a search queryor select a map feature. Likewise, the selection of an advertisementbased on a business location that corresponds to a location on the mapspace displayed on a user device presents advertisements that may not beof any interest to the user, e.g., a user may be viewing a map locationof a vacation destination and be presented a content item for a plumbingservice that is located near the vacation destination.

Thus, in some implementations, the advertisements 103 can be selectedfor presentation with a map space based on the advertisement's potentialrelevance to a user that is requesting the map space. Such potentialrelevance can be determined based on the interactions of previous userswith the map space, as discussed below. Accordingly, advertisements 103that are likely relevant to the interests of the user that requested themap space can be provided prior to the user taking any action within themap space of the map 109.

§2.0 Content Item Selection

FIGS. 2A and 2B are examples of a map 200 at different zoom levels andfor which advertisements can be selected for presentation. Inparticular, FIG. 2A depicts the map 200 at a first zoom level in adisplay region 204, such as a web browser window, and FIG. 2B depictsthe map 200 at a second zoom level in the display region 204. Each ofthe maps in FIGS. 2A and 2B can have advertisements presented when themaps 200 are first displayed in the display region 204 and withoutfurther user interactions. These advertisements are selected fromcontent targeting data associated with a map space 202. The contenttargeting data is based on input data, e.g., search queries and featureselections, that have been received from display interfaces in thedisplay region 204 in response to presentations of the map space toother users prior to an instant request for the map space.

The map 200 at the first zoom level shows a map space 202 that is theportion of the map 200, selected by the map server that can be displayedon a user device in response to a request for the map space 202. In someimplementations, the map space 202 is defined by the display region 204and a zoom level. The zoom level represents the scaling of the map space202 within the display region 204, and is graphically indicated by theposition of a zoom bar 220 in a zoom control 222 in the display region204.

The map space 202 that is presented to the user device can include mapfeature items 203, such as text items 203 a that identify features onthe map and/or graphic items 203 b (e.g., a public park icon or amonument icon) that graphically illustrate map features. Additional mapfeature items 203 can include photos of landmarks or other points ofinterest, and icons that represent the location of a category offeatures (e.g., restaurants, coffee shops, nightclubs, or churches) onthe map space 202. The feature items 203 can be rendered as part of themap space 202.

Some feature items 203 can be selectively enabled or disabled by a userthrough a first display interface, e.g., a feature menu 205, in thedisplay region 204. For example, a user that is interested in viewingrestaurant locations in the map space 202 can enable a map feature thatplaces icons on the map space 202 that identifies the location of therestaurants. The feature items 203 can be selectively enabled ordisabled by selecting a feature label 207 corresponding to the featureitem 203 in a feature menu 205. The feature menu 205 can be included inthe display interface 205 enabling users to click predefined selections.For example, a user can click on a feature label 207 that corresponds tothe feature item 203 that the user would like to enable or disable. Insome implementations, data can be submitted to the map server to reportthe selection of the feature label 207. In response to receiving thedata, the map server can provide additional map data that can be used tofacilitate display of the feature item 203 on the map space 202.

In some implementations, users can submit queries through a seconddisplay interface 209 a, e.g., a search interface 209 a with a text box209 b that is provided in the display region 204. The search interface209 a can, for example, allow users to type the query in text box 209 band submit the query to the map server 112.

Other display interfaces can also be used in the display region 204.Examples include selectable map widgets displayed on the space 202, suchas building or business icons, rulers or path tracing tools that a usercan use to measure distances or plot paths, to name just a few.

Also depicted in the map space 202 is a map location 210, as indicatedby a dot. In the example of FIGS. 2A and 2B, the map location 210 is anapproximate center location of the map spaces 202 and 230 that arepresented in the display region 204. The representation of the maplocation 210 may not be shown on the map space 202, and thus can beconceptual. In other implementations, a dot, geographic coordinates, orother representation can be displayed in the map space 202 to representthe map location 210. The map location 210 can be a business address,e.g., “123 Third Avenue,” or can be latitude and longitude coordinates,or any other identifier that represents the location of the map space202 on the map 200.

When the map location 210 is a single reference point, similar mapspaces may have map locations that are located within a common portionof the map. In some implementations, all map locations that are within aspecified range of a reference map location can be associated with thereference map point. For example, if the map space 202 of FIG. 2A isrepositioned slightly in any direction then the new map location willsimilarly be adjusted slightly. However, the data associated with therepositioned map space can be substantially the same as the originallypresented map space 202. Therefore, rather than storing all of the dataassociated with the new map space as separate data that is associatedwith the new map location, the repositioned map space can be associatedwith the map location 210. For example, map locations within a map tile,a latitude/longitude range, or any other common area of a map can beassociated with a single map location.

Other conventional map symbols, e.g., streets and city blocks, areillustrated by the conventional block and street symbols as shown inFIG. 2A. Some of these elements are omitted to avoid congestion of thedrawings.

Input data that have been received by use of the display interfaces 205or 209, or other display interfaces, can be used to identify contenttargeting data associated with the map space 202. This content targetingdata can then be used to select advertisements for display in the mapspace 202, or some other portion of the display region, each time themap space is requested. For example, placement locations 206 a, 206 bcan be allocated for presentation of advertisements in the map space202, and advertisements based on the content targeting can be selectedand displayed when the map space is 202 is initially displayed in thedisplay region 204. Preferably, these advertisements are displayed so asnot to preclude map features, such as features 203, buildingabstractions 211, or other features, such as park or monument symbols.Similarly, the advertisements can be presented in a map sidebar 213, asillustrated in the map space 230 of FIG. 2B.

Advertisement selection improves initial advertisement targeting basedon a request for a particular map space, as the advertisements areselected based, in part, on aggregate signals of user interestsassociated with the map space 202. For example, input data that has beenreceived from previous users in response to the previous user'sselection of a feature item 203 or search query submission can be usedto generate content targeting data. In turn, the content targeting datacan be associated with the map location 210 of the map space 202 thatwas presented when the input data was received. This content targetingdata can be used to identify advertisements for which a probability ofbeing relevant to users requesting the map space has been determined.

Content targeting data and associated map locations can be stored andupdated over time to identify the frequency that the same or similarcontent targeting data is received in response to the presentation of amap space. For example, in the public park depicted in the map space202, an annual coffee festival may be held. While the public park mayotherwise have nothing to do with the topic of “coffee”, over time, astatistically significant number of queries related to coffee may beinput into the search interface 209 by users when viewing the map space202 of FIG. 2A. In turn, the content targeting data can be updated toaccount for these queries (e.g., increment a query count), and thuscontent targeting data related to the topic of “coffee” is associatedwith the map location 210. Thereafter, when users request the map space202, advertisements related to the topic of “coffee” may be initiallydisplayed with the map space 202.

In some implementations, that content targeting data is defined by themost-frequently used input data. For example, queries associated with amap space can be ranked according to input frequency, and the top Nqueries, e.g., the top three queries, can be used to selectadvertisements for the map space 202.

In other implementations, the content targeting data can also includetopics that are related to the input data. For example, clusters oftopic data can be generated based on the input data. These clusters oftopic data can include topics that are generated based on semantic rulesthat identify the topics as being related to the input data. Forexample, when the query “coffee” is received, the topics “latte,”“cappuccino,” and “coffee shop” can be generated. These topics can alsobe associated with the map location and stored to facilitateadvertisement selection.

Many known data clustering techniques can be used to create the clustersof topic data. Example data clustering techniques can includeagglomerative hierarchical clustering, partitional clustering, andspectral clustering, to name just a few.

In some implementations, a probability that the generated topics in theclustering data are related to the input data can be included in thecontent targeting data. The probability can be determined based on thesemantic rules used to generate the topics, and can be a measure ofcorrelation between the input data and the topics. For example, thetopics “latte,” “cappuccino,” and “coffee shop” can have respectiveprobabilities 0.1, 0.15, and 0.2 of being related to the query “coffee”based on the rules used to generate the topics. These probabilities canbe associated with the topics and stored as additional content targetingdata to facilitate content item selection.

In some implementations, the probability that input data is relevant toany user can be determined, for example, based on the frequency that theinput data is received relative to all queries. For example, the query“coffee” may be received once out of every one thousand queries.Therefore, the probability that an advertisement selected based on theinput data “coffee” is relevant to the interests of a user thatsubsequently requests the map space 202 can be 0.001, because one out ofone thousand users have submitted a query for “coffee” in response tothe presentation of the map space 202.

Similarly, probabilities that the topics included in the contenttargeting data are relevant to any user requesting the map space 202 canbe determined based on the measure of relatedness between the input dataand the topic. For example, the topic “coffee shop” can have aprobability 0.0002 (e.g., 0.2*0.001) of being relevant to any user thatrequests the map space 202. Similarly, the topics “latte,” and“cappuccino,” can have probabilities of 0.0001 and 0.00015,respectively, of being relevant to any user that requests the map space202. In some implementations, a topic's probability of being relevantcan be the sum the probability of the topic being relevant when receivedas input data and the probability that the topic is relevant whengenerated based on another input data. For example, if a topic receivedas input data has a 0.1 probability of being relevant to any user and a0.001 probability of being relevant when generated as a topic based onother input data, the total probability that the topic is relevant canbe 0.101.

The input data and topics that are included as content targeting datafor a map location 210 can be ranked based on the probabilities. Basedon the example above, a possible ranking of the topics “coffee,”“latte,” “cappuccino,” and “coffee shop” is provided in Table 1:

TABLE 1 Rank Topic Probability 1 Coffee .001 2 Coffee shop .0002 3Cappuccino .00015 4 Latte .0001

The topic ranking can be used to identify a subset of the contenttargeting data to use for advertisement selection. In someimplementations, a threshold probability can be used to identify thesubset of content targeting data. For example, topics that have aprobability higher than 0.00018 can be used to identify advertisementsfor presentation with the map space 202. In this example, the topics“coffee” and “coffee shop” are used to identify advertisements.Similarly, a threshold number of topics can be used to identifyadvertisements for presentation with the map space 202. The thresholdnumber of topics can be used independently or in conjunction with thethreshold probability. For example, a maximum of three topics can beused to identify advertisements. When the threshold number of topics isused independently, the topics “coffee,” “coffee shop,” and “cappuccino”can be used to identify advertisements. However, if the threshold numberof topics is used in conjunction with the threshold probability then thetopics used to identify advertisements can be limited to “coffee” and“coffee shop” by the threshold probability requirement.

In response to a request for the map space 202, the advertisements canbe selected, for example, by comparing the content targeting data toadvertisement data that is associated with the advertisements.Advertisers can include, for example, keywords as advertising data thatcan be used to target the advertisements. Therefore, the contenttargeting data can be compared to the keywords that are associated withthe advertisement to determine if the advertisement can be presented. Ifthe content targeting data corresponds to the keywords that areassociated with the advertisement, the advertisement can be selected forpresentation. For example, when the content targeting data includes thetopic “coffee,” an advertisement for a coffee shop including the keyword“coffee” can be selected for presentation.

In some implementations, the advertisement that is selected can be theadvertisement that has a keyword that corresponds to a topic in thecontent targeting data that has the highest probability of beingrelevant to the interests of a user that requested the map. For example,continuing with the above example, if the topics “coffee” and “coffeeshop” are used to select an advertisement, then an advertisement thathas the keyword “coffee” can be selected for presentation in the mapspace 202 because the topic “coffee” has a higher probability of beingrelevant to the interests of a user that requests the map space 202.

In some implementations, contextual data can be included in the contenttargeting data. The contextual data can be derived from content (e.g.,text) that is presented adjacent to the map space. For example, a mapspace may be presented on a web page adjacent to textual content relatedto cars, such as on a webpage related to an automotive topic. In somesituations, the term “cars” alone may not provide enough information todetermine which car related advertisements (e.g., car repair shop, cardealership, car rentals, etc.) might be relevant to a user that requeststhe web page. Similarly, the query “repair shops,” may be the query mostreceived when the map space is presented. In some situations, the query“repair shops” alone might not provide enough information to determinewhich repair shop advertisement (e.g., car, watch, vacuum cleaner, etc.)might be relevant to a user that requests the web page.

However, the query “repair shops” can be combined with the contextualdata “cars” so that the content targeting data includes “cars” and“repair shops.” In turn, the content targeting data including “cars” and“repair shop” may be more likely to correspond to an advertisement forthe car repair center than the advertisements for the other repaircenters (e.g., watch or vacuum cleaner) or the other car advertisements(e.g., dealership, rentals). Thus, combining contextual data presentedadjacent to the map space can be included in the content targeting datato facilitate selection of content items in response to a request for amap space.

In some implementations, the content targeting data that are associatedwith a map location 210 can depend on the zoom level of the map space202. FIG. 2B depicts the map 200 at a lower zoom level, e.g., a “zoomedout” view relative to the view of FIG. 2A, as indicated by the adjustedposition of the zoom bar 220 in the zoom control 222.

At the lower zoom level of FIG. 2B, a map space 230 is displayed in thedisplay region 204. The map space 230 includes a larger portion of themap 200 and includes the map space 202 that was presented in FIG. 2A.Users that requests the map space 230 may have different interests thanthe users that requested the map space 202. In turn, the user may alsoselect feature items 203 and submit queries that differ from thosesubmitted while map space 202 was presented. Accordingly, contenttargeting data can be associated with the map space 230 that isdifferent than the content targeting data that was associated with mapspace 202.

In some implementations, separate content targeting data can bemaintained for each zoom level relative to a particular map location210. For example, first content targeting data can be maintained for themap location 210 at the zoom level that results in the presentation ofmap space 202. Similarly, second content targeting data can bemaintained for the map location 210 at the zoom level that results inthe presentation of map space 230. Therefore, for each map space 202 and230, advertisements can be independently selected that are likelyrelevant to respective users' interests.

In some implementations, the number of advertisements selected can bebased in part on the zoom level of the map space. For example, athreshold zoom level can be identified, above which no advertisementswill be selected for presentation. Similarly, a threshold number ofadvertisements can be identified for presentation for each zoom level ofeach map location 210. In some implementations, the number ofadvertisements that are selected can depend on the feature density ofthe map space. For example, if the density of the feature items 203 onthe map space is high, there may be little room remaining for placementpositions. Therefore, placing advertisements in the map space may resultin a crowded map space. In turn, the utility of the map 200 may bediminished by adding advertisements to a crowded map space, because themap 200 can be difficult to use. For example, if advertisements areoccluding feature items 203 a user may not be able to identify alocation of interest.

§3.0 Example Content Item Selection System

FIG. 3 is a block diagram of an example content item selection system300. The system 300 can be used to realize the features as discussedabove, e.g., selection of content items for placement in requested mapspaces based on content targeting data that can include a probabilitythat the content item is relevant to the interests of a user thatrequested the map space.

The system 300 can include a content item selection module 302. Thecontent item selection module 302 can be implemented, for example, inthe map server 112, as described in FIG. 1. The content item selectionmodule 302 is in data communication with a content targeting data store304 and a placement location data store 306. The content targeting datastore 304 and placement location store 306 can each be implemented, forexample, in the map store 114, or in any other storage medium.

In some implementations, the content item selection module 302 canidentify advertisements for placement based on the content targetingdata that is stored in the content targeting data store 304. In turn,the content item selection module 302 can provide selectedadvertisements for presentation in placement positions that are definedby placement locations that are stored in the placement location datastore 306. Alternatively, the content item selection module 302 canprovide advertisements for display adjacent to a map space, such as inthe map sidebar 213 of FIG. 2B.

§3.1 Content Targeting Data Store

The content targeting data store 304 can store content targeting datathat can be used to identify advertisements for presentation in a mapspace, e.g., the map space 202 or 230. For example, the contenttargeting data can include input data that is received from a userdevice while the map space is presented. In some implementations, thecontent targeting data can further include topics that are generatedbased on the input data and semantic rules. The content targeting datacan further include a probability that each topic is relevant to theinterests of a user that requested the map space. The content targetingdata is associated with a map location, e.g., coordinates correspondingto the map location.

As discussed above, the content targeting data for a particular maplocation can differ at each zoom level. Thus, each zoom level for a maplocation can have a separate content targeting data record. In turn,different advertisements can be selected for the map location atdifferent zoom levels. In some implementations, the number ofadvertisements can similarly depend on the zoom level. Above a thresholdzoom level, advertisements can be omitted from the map space. Similarly,when a selected feature item density is realized, advertisements can beomitted from the map space to prevent cluttering the map space. Thus, insome implementations, the content targeting data store 304 can index thecontent targeting data records according to the zoom level and the maplocation to which the content targeting data is related.

The map location can be a defined point on the map (e.g., coordinatelocation). In some implementations, content targeting data for multiplemap locations can be consolidated into a single content targeting datarecord. For example, a map can be divided into a grid, and each portionof the grid can correspond to a particular portion (e.g., geographicregion) of the map. In turn, content targeting records that areassociated with a map location that is within the particular portion ofthe map can be consolidated into a single content targeting data record.The content targeting data can be stored according to the map locationwith which the data is associated to facilitate retrieval of the contenttargeting data in response to a map space request. For example, given amap location that corresponds to the map space request, contenttargeting data that are associated with a map location that is in thesame grid region can be identified for retrieval, while contenttargeting data outside the grid region can be ignored.

An example content targeting data structure 305 is illustrated in FIG.3. Content targeting data records for two zoom levels, Z1 and Z2, aredepicted. Each content targeting data record for the first zoom level,Z1, is indexed by its map location (e.g., Locations 11, 12 . . . 1 m)and zoom level (e.g., Zoom Z1). Each content targeting data record alsoincludes content targeting data (e.g., T1, T2 . . . Tm) that include thetopic data that were generated in response to previous user interactionswith the corresponding map space. For example, if the content targetingrecord 11 contains content targeting data for the map space 202 of FIG.2A, then the map location 11 can correspond to map location 210 and thezoom level Z1 can correspond to the zoom level of map space 202.Accordingly, when a map space that corresponds to map location 210 isrequested at zoom level Z1 the content targeting data record 11 can beretrieved to facilitate identification of advertisements forpresentation with the map space.

Similarly, each content targeting data record at the second zoom level,Z2, which corresponds to the zoom level of FIG. 2B, is identified andindexed by its map location (e.g., Locations L21, L22 . . . L2 n) andzoom level (e.g., Zoom Z2). Each of these content targeting records alsoincludes content targeting data (e.g., T1, T2 . . . Tn) that include thetopic data that were generated in response to previous user interactionswith the corresponding map space. Thus, when a map space thatcorresponds to map location 210 is requested at zoom level Z2 thecontent targeting data record 21 can be retrieved to facilitateidentification of advertisements for presentation with the map space.Likewise, the remaining content targeting data records 2 . . . n cancorrespond to the map spaces that are associated with additional maplocations.

§3.2 Placement Location Data Store

Advertisements that are selected in response to the content data can bedisplayed in the map sidebar 213 or on the map space, e.g., map space230. If advertisements are to be rendered on the map space, then anoptional placement location data store 306 can store placement locationsfor advertisements at multiple zoom levels. In some implementations, theplacement locations for an advertisement can be stored in a contentplacement data structure 307. Each advertisement can have an associatedmap location ML, and a plurality of placement location data stored inplacement records. The placement location data are based on the maplocation ML and zoom level Z1 . . . Zq, and can define placementlocations for the advertisement based on the map location ML so thatwhen a map space is presented at the zoom level with the advertisement,the advertisement does not interfere with map feature items that arepresented on the map space at the zoom level or otherwise clutter themap space.

For example, location ML can correspond to the map location 210 shown inthe map spaces 202 and 230 of FIGS. 2A and 2B. The records indexed bythe zoom level Z1 and corresponding placement locations L11 and L12 cancorrespond to the locations 206 a and 206 b graphically depicted in themap space 202 of FIG. 2A. Likewise, the record indexed by the zoom levelZ2 and corresponding location L21 can correspond to the location 206 cgraphically depicted in the map space 230 of FIG. 2B.

§3.3 Content Item Selection Module

The content item selection module 302 is responsible for selectingadvertisements for presentation with the map space. The content itemselection module 302 can be implemented, for example, in the map server112.

§3.3.1 Content Item Selection

The content item selection module 302 can receive notification that amap space has been requested and can identify the map location and thezoom level associated with the map space. The map location can be eitherpre-associated with the map space, (e.g., a center location for the mapspace), or can be assigned to the map space as the result, for example,of the pre-assigned map space having a map location that is within adefined proximity of a map location that is associated with apre-existing content targeting data record. In either case, the contentitem selection module 302 queries the content targeting data store 304for a corresponding content targeting data record.

For example, when a map space is requested from the map server 112 ofFIG. 1, the map server 112 can provide information that identifies themap location 210 for the map space to the content item selection module302. The content item selection module 302 can use the map location 210and zoom level to identify the content targeting data that is required.In turn, the content item selection module 302 can retrieve theappropriate content targeting data record for the geographic region fromthe content targeting data store 304 as indexed by the zoom level atwhich the map space corresponding to the map location is to bedisplayed. Thus, for the map location 210 at the zoom level Z1, thecontent targeting data record L11 can be retrieved. Advertisements canthen be selected based on the content targeting data contained incontent targeting data record L11. Likewise, for the map location 210 atthe zoom level Z2, the content targeting data record L21 can beretrieved, and the advertisement can then be selected based on thecontent targeting data contained in content targeting data record L21.

In some implementations, the content item selection module 302 canprovide the information from the content targeting data record to theadvertisement server 104 of FIG. 1 to receive advertisements thatcorrespond to the content targeting data in the record. Theadvertisement server 104 can identify advertisements for presentationwith a map environment, for example, based on keywords that areassociated with content targeting data, e.g., keywords.

In other implementations, the content item selection module 302 cangenerate a virtual web page based on the corresponding content targetingdata for a map space. A virtual web page can be created if, for example,the advertising server 104 is configured to provide content items, e.g.,advertisements, based on web page content. For example, in response to auser requesting the map space 202 of FIG. 2A, the advertisement server104 can receive a request for advertisements from a virtual web pageincluding a page title “Coffee,” and which contains a headline “CoffeeShop,” and the text “Cappuccino” as an article. In turn, theadvertisement server 104 can identify the appropriate content for avirtual web page based on an advertisement relevancy determinationalgorithm that takes into account keywords and the placement of thekeywords in a web page. For example, the advertisement server 104 canuse the identified topics to select advertisements that are associatedwith the keywords “coffee,” “coffee shop,” and “cappuccino.”

Additionally, the advertisement server 104 can rank the advertisementsin an order in which they should be selected for presentation. Thisranking can be done, for example, based on the type of content thatresulted in the selection of the advertisement. In some implementations,the advertisements that are selected based on a title can be rankedhighest, advertisements selected based on a header can be ranked afterthose selected based on title content, and advertisements selected basedon article content can be ranked after those selected based on headercontent. Thus, in the above example, advertisements can be ranked forselection and/or display as provided in Table 2.

TABLE 2 Topic Page Location Rank Coffee Title 1 Coffee shop Header 2Cappuccino Article 3

The virtual page location for each topic of the content targeting datacan be determined based on the probability of relevance that isassociated with the topic. Threshold probabilities can define categoriesof title, header, or article content for the content targeting data. Forexample, a probability of 0.001 or higher associated with a topic in thecontent targeting data can define a title topic, a probability of 0.005or higher can define a header topic, and topics associated with allother probabilities can be considered article content. Thus, theadvertisement server 104 can identify advertisements for presentationwith map environments, regardless of the textual content included withthe map environment, in a manner similar to that for other web pagesthat request advertisements.

§3.3.2 Generating Content Targeting Data

In some implementations, the content item selection module 302 cangenerate a content targeting data record based on first input data thatis received while a map space is presented. The content item selectionmodule 302 can update the content targeting data records based onsubsequent input data that is received while the map space is presented.After the content targeting data is generated, it can be stored in thecontent targeting data store 304 as a content targeting data record thatis associated with a map location.

In some implementations, the content item selection module 302 cangenerate content targeting data for a map location based on a queryreceived from a user device while the map space is presented. Forexample, the content item selection module 302 can receive the query“coffee” from a user device while the map space of FIG. 2A is presented.In turn, the content item selection module 302 can create or update acontent targeting data record for the map location 210 and zoom levelthat corresponds to the map space 202 to include the topic “coffee”.

The content item selection module 302 can also generate contenttargeting data for a map location based on the selection of a featureitem. In some implementations, the selection of a feature item can bemapped to a corresponding keyword. For example, if input data isreceived from a user device indicating the selection of a feature thatpresents icons representing the locations of all restaurants locatedwithin the map space, then the content item selection module 302 caninclude the topic “restaurant” in the content targeting data record thatcorresponds to the map location and zoom level.

The content item selection module 302 can generate additional topicsbased on the input data as, described above. In some implementations,the content item selection module 302 can generate topics that arerelated to the input data. For example, based on the input data“restaurants,” the content item selection module 302 can generate thetopics “cafe” and “deli.” In turn, these topics can be included in thecontent targeting data record that corresponds to the map location andzoom level of the map space presented when the input data was received.

In some implementations, the content item selection module 302 cangenerate the topics by applying semantic rules to the input data. Thesemantic rules can identify the topics, for example, based on acorrelation between the meaning and use of the topics and the inputdata. Based on this correlation, the content item selection module 302can assign a measure of relatedness (e.g., probability that the topic isrelated to the input data) to the topic.

Based on the measure of relatedness, the content item selection module302 can generate a probability that the topic is relevant to the userinterest of a user that requests the map space corresponding to thecontent data record. For example, the content item selection module 302can maintain a count of the number of requests for a particular mapspace, the total count of input data that are received, and countsidentifying the number of times that each topic is received as inputdata. The content item selection module 302 can use these counts todetermine the percentage of total input data that correspond to eachtopic as well as the percentage of all map requests for which each topicis received as input data. Based on these percentages, the probabilitythat each topic is relevant to any user that requests the map space canbe determined. The content item selection module 302 can store theseprobabilities with the topics in the content targeting data record tofacilitate selection of advertisements, as described above.

§4.0 Example Process Flow

FIG. 4 is a flowchart of an example process 400 of selecting a contentitem for presentation with a map. The process 400 can be used, forexample, in the map server 112 of FIG. 1 and/or the content itemselection module 302 of FIG. 3.

Stage 402 receives input data in response to presentation of a mapspace. In some implementations, the input data can correspond to datareceived based on selection of a feature item or a query received whilethe map space is presented. The input data can be received, for example,by the map server 112 of FIG. 1 and/or the content item selection module302 of FIG. 3.

Stage 404 generates content targeting data based on the input data. Insome implementations, content targeting data can include the input dataand topics related to the input data. The content targeting data can begenerated, for example, by the map server 112 of FIG. 1 and/or thecontent item selection module 302 of FIG. 3.

Stage 406 associates the content targeting data with a map locationcorresponding to the map space. In some implementations, the contenttargeting data can be associated with the map location based on a zoomlevel. The association can be performed, for example, by the map server112 of FIG. 1 and/or the content item selection module 302 of FIG. 3.

Stage 408 receives a request for the map space. In some implementations,the map space is a portion of a map for presentation in a display regionon a user device. The request for the map space can be received, forexample, by the map server 112 of FIG. 1 and/or the content itemselection module 302 of FIG. 3.

Stage 410 identifies content targeting data associated with the mapspace. In some implementations, the content targeting data is based oninput data that have been received from display interfaces in responseto presentations of the map space. The input data can be received in thedisplay interfaces prior to the request for the map space. In someimplementations, the content targeting data can be identified byidentifying a map location associated with the map request andidentifying the content targeting data that is associated with the maplocation. The content targeting data can be identified, for example, bythe map server 112 of FIG. 1 and/or the content item selection module302 of FIG. 3.

Stage 412 identifies a content item for display with the map space basedon the content targeting data. In some implementations, the content itemcan be identified by the map server 112 of FIG. 1 and/or the contentitem selection module 302 of FIG. 3.

Stage 414 provides the content item for display in the display region onthe user device in response to the request for the map space. The mapserver 112 of FIG. 1 and/or the content item selection module 302 ofFIG. 3 can provide the content item for display in the display region.

FIG. 5 is a flowchart of an example process 500 of generating contenttargeting data based on input data. The process 500 can be used, forexample, in the map server 112 of FIG. 1 and/or the content itemselection module 302 of FIG. 3. The process 500 can be used, forexample, to implement stage 404 of FIG. 4.

Stage 502 generates content targeting data based on the input data andsemantic rule. In some implementations, the semantic rules can be usedto generate content targeting data that are semantically related (e.g.,correspond to the meaning) of the input data. The content targeting datacan be generated, for example, by the map server 112 of FIG. 1 and/orthe content item selection module 302 of FIG. 3.

Stage 504 identifies the content targeting data that satisfies athreshold measure of relatedness between the content targeting data andthe map space. In some implementations, the threshold measure ofrelatedness can be a threshold probability that the content targetingdata is relevant to the interests of a user that requests the map space.The content targeting data identification can be performed, for example,by the map server 112 of FIG. 1 and/or the content item selection module302 of FIG. 3.

Stage 506 ranks the content targeting data based on the measure ofrelatedness between the content targeting data and the map space. Insome implementations, the measure of relatedness can be a probabilitythat the content targeting data is related to the map space. In someimplementations, the measure of relatedness can be a probability thatthe content targeting data is relevant to the interests of a user thatrequests the map space. The ranking can be performed, for example, bythe map server 112 of FIG. 1 and/or the content item selection module302 of FIG. 3.

FIG. 6 is block diagram of an example computer system 600 that can beused to facilitate selection of content items for presentation withonline maps. The system 600 includes a processor 610, a memory 620, astorage device 630, and an input/output device 640. Each of thecomponents 610, 620, 630, and 640 can be interconnected, for example,using a system bus 650. The processor 610 is capable of processinginstructions for execution within the system 600. In one implementation,the processor 610 is a single-threaded processor. In anotherimplementation, the processor 610 is a multi-threaded processor. Theprocessor 610 is capable of processing instructions stored in the memory620 or on the storage device 630.

The memory 620 stores information within the system 600. In oneimplementation, the memory 620 is a computer-readable medium. In oneimplementation, the memory 620 is a volatile memory unit. In anotherimplementation, the memory 620 is a non-volatile memory unit.

The storage device 630 is capable of providing mass storage for thesystem 600. In one implementation, the storage device 630 is acomputer-readable medium. In various different implementations, thestorage device 630 can include, for example, a hard disk device, anoptical disk device, or some other large capacity storage device.

The input/output device 640 provides input/output operations for thesystem 600. In one implementation, the input/output device 640 caninclude one or more of a network interface devices, e.g., an Ethernetcard, a serial communication device, e.g., an RS-232 port, and/or awireless interface device, e.g., and 802.11 card. In anotherimplementation, the input/output device can include driver devicesconfigured to receive input data and send output data to otherinput/output devices, e.g., keyboard, printer and display devices 660.Other implementations, however, can also be used, such as mobilecomputing devices, mobile communication devices, set-top box televisionclient devices, etc.

The content item selection module 302 can be realized by instructionsthat upon execution cause one or more processing devices to carry outthe processes and functions described above. Such instructions cancomprise, for example, interpreted instructions, such as scriptinstructions, e.g., JavaScript or ECMAScript instructions, or executablecode, or other instructions stored in a computer readable medium. Thecontent item selection module 302 can be distributively implemented overa network, such as a server farm, or can be implemented in a singlecomputer device.

Although an example processing system has been described in FIG. 6,implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in other types ofdigital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.Implementations of the subject matter described in this specificationcan be implemented as one or more computer program products, i.e., oneor more modules of computer program instructions encoded on a tangibleprogram carrier for execution by, or to control the operation of, aprocessing system. The computer readable medium can be a machinereadable storage device, a machine readable storage substrate, a memorydevice, a composition of matter effecting a machine readable propagatedsignal, or a combination of one or more of them.

The term “processing system” encompasses all apparatus, devices, andmachines for processing data, including by way of example a programmableprocessor, a computer, or multiple processors or computers. Theprocessing system can include, in addition to hardware, code thatcreates an execution environment for the computer program in question,e.g., code that constitutes processor firmware, a protocol stack, adatabase management system, an operating system, or a combination of oneor more of them.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program does notnecessarily correspond to a file in a file system. A program can bestored in a portion of a file that holds other programs or data (e.g.,one or more scripts stored in a markup language document), in a singlefile dedicated to the program in question, or in multiple coordinatedfiles (e.g., files that store one or more modules, sub programs, orportions of code). A computer program can be deployed to be executed onone computer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

Computer readable media suitable for storing computer programinstructions and data include all forms of non volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto optical disks; andCD ROM and DVD ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back endcomponent, e.g., a data server, or that includes a middleware component,e.g., an application server, or that includes a front end component,e.g., a client computer having a graphical user interface or a Webbrowser through which a user can interact with an implementation of thesubject matter described is this specification, or any combination ofone or more such back end, middleware, or front end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet. The computing system caninclude clients and servers. A client and server are generally remotefrom each other and typically interact through a communication network.The relationship of client and server arises by virtue of computerprograms running on the respective computers and having a client serverrelationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or of what may be claimed, but rather as descriptions offeatures that may be specific to particular implementations ofparticular inventions. Certain features that are described in thisspecification in the context of separate implementations can also beimplemented in combination in a single implementation. Conversely,various features that are described in the context of a singleimplementation can also be implemented in multiple implementationsseparately or in any suitable subcombination. Moreover, althoughfeatures may be described above as acting in certain combinations andeven initially claimed as such, one or more features from a claimedcombination can in some cases be excised from the combination, and theclaimed combination may be directed to a subcombination or variation ofa subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

This written description sets forth the best mode of the invention andprovides examples to describe the invention and to enable a person ofordinary skill in the art to make and use the invention. This writtendescription does not limit the invention to the precise terms set forth.Thus, while the invention has been described in detail with reference tothe examples set forth above, those of ordinary skill in the art mayeffect alterations, modifications and variations to the examples withoutdeparting from the scope of the invention.

1. A computer-implemented method, comprising: receiving a request for amap space, wherein the map space is a portion of a map for presentationin a display region on a user device; identifying content targeting dataassociated with the map space, the content targeting data based on inputdata that have been received from display interfaces in the displayregion in response to presentations of the map space prior to therequest for the map space; identifying a content item for display withthe map space based on the content targeting data; and providing thecontent item for display in the display region on the user device inresponse to the request for the map space.
 2. The method of claim 1,wherein identifying content targeting data comprises: identifying a maplocation associated with the map request; and identifying contenttargeting data that is associated with the map location.
 3. The methodof claim 2, further comprising: generating the content targeting databased on the input data; associating the content targeting data with amap location corresponding to the map space.
 4. The method of claim 3,further comprising ranking the associated content targeting data basedon a measure of relatedness to the map space.
 5. The method of claim 4,wherein the measure of relatedness is a probability that the contenttargeting data is relevant to a user that requests the map space.
 6. Themethod of claim 4, wherein generating the content targeting data that isbased on the input data comprises: generating content targeting databased on the input data and semantic rules; and identifying the contenttargeting data that satisfies a threshold measure of relatedness betweenthe content targeting data and the map space.
 7. The method of claim 6,wherein generating content targeting data based on the input data andsemantic rules comprises generating expansion topics that aresemantically related to the data input.
 8. The method of claim 7,further comprising ranking the content targeting data based on themeasure of relatedness between the content targeting data and the mapspace.
 9. The method of claim 6, wherein identifying content targetingdata that correspond to the map space comprises selecting a maximumnumber of content targeting data based on the measure of relatednessbetween the content targeting data and the map space.
 10. The method ofclaim 9, further comprising: providing the maximum number of contenttargeting data to a content item selector; and receiving the contentitem for display in the display region on the user device in response tothe request for the map space.
 11. The method of claim 10, wherein thecontent item is selected based on a feature density of the map space.12. The method of claim 1, wherein the input data are search queries.13. A computer-implemented method, comprising: receiving input data fromuser devices, the input data having been input in display interfacesdisplayed on user devices; associating the input data with map spaces,each data input being associated with a map space that was displayed ina display region on a user device when the data input was input in thedisplay interface at the user device; for each map space, generatingcontent targeting data based on the associated input data; providingaccess to the content targeting data for each map space to facilitateidentification of content items for display with the map spaces.
 14. Themethod of claim 13, wherein receiving input data comprises receivingsearch queries that are received in response to presentation of the mapspace.
 15. The method of claim 13, further comprising: receiving arequest for one of the map spaces from a requesting device; identifyingthe content targeting data associated with the requested map space;identifying a content item based on the content targeting data; andproviding the identified content item to the requesting device forpresentation.
 16. The method of claim 15, wherein identifying thecontent targeting data comprises identifying the content targeting datathat satisfies a threshold measure of relatedness to the requested mapspace.
 17. The method of claim 15, wherein the content item is providedfor presentation adjacent to the map space.
 18. The method of claim 13,wherein identifying a content item comprises identifying the contentitem that corresponds to a highest ranked content targeting datum. 19.The method of claim 13, wherein receiving input data comprises receivingdata in response to mouse clicks on the map space.
 20. The method ofclaim 13, further comprising: ranking the content targeting data basedon a measure of relatedness between the content targeting data and themap space.
 21. The method of claim 20, wherein providing access to thecontent targeting data for each map space comprises providing access toa maximum number of content targeting data based on the ranking.
 22. Themethod of claim 21, wherein receiving input data comprises receivingdata that corresponds to selection of a map feature.
 23. The method ofclaim 22, wherein the map feature comprises a place of interest displayfor the map space.
 24. The method of claim 22, wherein identifyingcontent items comprises: identifying a frequency of selection for themap feature for the map space; ranking the map features based on thefrequency of selection; and identifying a content item based on the mapfeature ranking.
 25. The method of claim 13, wherein associating theinput data with map spaces comprises generating clusters of topic datathat correspond to common map spaces.
 26. The method of claim 25,wherein generating the clusters of topic data comprises generatingtopics that correspond to the input data.
 27. The method of claim 13,wherein the content item is an online advertisement.
 28. Software storedin computer readable media and comprising instructions executable by aprocessing device and upon such execution cause the processing device toperform operations comprising: receiving a request for a map space,wherein the map space is a portion of a map for presentation in adisplay region on a user device; identifying content targeting dataassociated with the map space, the content targeting data based on inputdata that have been received from display interfaces in response topresentations of the map space in display regions prior to the requestfor the map space; identifying a content item for display with the mapspace based on the content targeting data; and providing the contentitem for display in the display region on the user device in response tothe request for the map space.
 29. A system, comprising: a contenttargeting data store storing content targeting data associated with amap space, the content targeting data based on input data that have beenreceived from display interfaces in response to presentations of the mapspace in display regions; and a content item selection module configuredto receive a request for a map space and in response provide theassociated content targeting data to a content selector for selection ofcontent items related to the content targeting data to be displayed withthe map space on a user device.